Agentic Coding

planning-with-files is a persistent file-based planning skill for AI coding agents that addresses context window loss during long-running tasks. The system uses three markdown files (task_plan.md, findings.md, progress.md) stored on disk to preserve execution state across context resets, crashes, and /clear commands. Key features include lifecycle hooks that re-inject plan context at the start of each turn, SHA-256 attestation to prevent plan tampering, parallel-write guards for multi-agent scenarios, and optional gated mode with a completion gate that prevents premature session termination. The skill has achieved 96.7% pass rate in evaluations and demonstrated 5.0-turn recovery after context wipe versus 13.3 turns without planning files. It integrates with 60+ coding agents including Claude Code, Cursor, GitHub Copilot, Hermes Agent, and Pi through both native plugins and the open Agent Skills standard, with setup taking under one minute. For solo SaaS founders using Claude Code and Cursor daily, this directly addresses efficient multi-step workflow management and context window optimization through persistent markdown-based execution tracking.

Context Mode solves context window optimization for AI-assisted coding by sandboxing raw data output—preventing tool calls like Playwright snapshots (56 KB → 299 B) and GitHub issue batches (59 KB → 1.1 KB) from flooding your context. It captures every file edit, git operation, task, and error into SQLite with FTS5 indexing, allowing session continuity across context compaction: when the model compacts to free space, Context Mode retrieves only relevant prior events via BM25 search rather than dumping everything back. The system supports 17+ platforms (Claude Code, Gemini CLI, VS Code Copilot, Cursor, OpenCode, Codex CLI, and others) with hooks that automatically enforce sandbox routing—redirecting dangerous commands and large outputs to the ctx_execute sandbox instead of raw Bash/Read calls. For developers using Claude Code or Cursor daily, Context Mode provides AGENTS.md-style routing files (CLAUDE.md for Claude Code) that teach the model to write scripts for analysis instead of reading files directly (47 × Read calls = 700 KB → 1 × ctx_execute = 3.6 KB). Session snapshots preserve working state across restarts and compaction events, indexed as searchable events so model picks up exactly where you left off without re-prompting.

Oh-My-ClaudeCode is a multi-agent orchestration framework for Claude Code that enables advanced agentic coding workflows. The platform features Team mode as the canonical orchestration surface with a staged pipeline (plan → PRD → execute → verify → fix), intelligent agent routing with 19 specialized agents, and automatic cost optimization saving 30-50% on tokens. For entrepreneurs and developers using Claude Code daily, it offers natural-language task description without memorized commands, persistent execution until verification completes, and automatic skill extraction to reuse solved patterns across projects. Key features include Autopilot for autonomous end-to-end execution, Execute for verified task completion, Deep Interview for requirements clarification via Socratic questioning, and integration with external providers like Codex, Gemini, Antigravity, and Cursor for cross-model code review and design validation. The framework emphasizes zero configuration setup, real-time HUD visibility into orchestration metrics, and project-scoped skill management through .omc/skills for team collaboration.

Claude Fable 5.1 introduces improvements for agentic coding workflows with a 1M token context window and adaptive thinking mode optimized for long-horizon work. Key features include per-message effort control (beta), turn-scoped system messages (beta), readable progress updates between tool calls, and significantly reduced cache read costs at a quarter of previous rates. The model maintains the same input/output pricing as Claude Fable 5 while delivering stronger performance for multistep research and document work, making it recommended for demanding reasoning tasks and extended agentic operations where Claude Opus 5 evaluation results fall short.